Identification of important locational, physical and economic dimensions in power system transient stability margin estimation

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Abstract

Increasing renewable generation can lead to significant spatial and temporal changes to the rotor angle stability boundary, such that critical contingencies may drastically change. Additionally, the inherent variability of renewables increases the number of operational scenarios that require stability assessment. This paper presents a methodology whereby a series of location-specific Decision Tree Regressors are trained, using power system variables to estimate the Critical Clearing Time (CCT) on a locational basis throughout a network. Permutation feature importance is used to reveal the most important power system variables for CCT estimation at each location (capturing aspects related to physical system characteristics, operational parameters as well as economic dispatch). Consequently, estimation of the duration and location of the critical fault can also be made – along with identification of important system variables that explicitly impact the critical fault. Results on the IEEE 39-bus network show accurate estimation of locational CCTs, with a mean absolute percentage error of 1.19% on average. Moreover, the mean absolute percentage error for the minimum CCT is 0.49%. An analysis of important power system variables is provided, demonstrating how the method can assist in the design of targeted locational interventions to improve the stability margin at specific locations.
Original languageEnglish
Pages (from-to)1135-1146
Number of pages12
JournalIEEE Transactions on Sustainable Energy
Volume13
Issue number2
Early online date25 Feb 2022
DOIs
Publication statusPublished - 25 Feb 2022

Keywords

  • critical clearing time
  • machine learning
  • power system stability
  • renewable generation
  • transient stability

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